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![LangChain](https://pica.zhimg.com/50/v2-56e8bbb52aa271012541c1fe1ceb11a2_r.gif)





Redis[#](#redis "Permalink to this headline")
=============================================

> 
> [Redis（远程字典服务器)](https://en.wikipedia.org/wiki/Redis)是一个内存数据结构存储器，用作分布式、内存键-值数据库、缓存和消息代理，可选持久性。
> 
> 
> 

本教程展示如何使用与[Redis向量数据库](https://redis.com/solutions/use-cases/vector-database/)相关的功能。

```python
!pip install redis

```

我们想要使用`OpenAIEmbeddings`，因此我们必须获取OpenAI API密钥。

```python
import os
import getpass

os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')

```

```python
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores.redis import Redis

```

```python
from langchain.document_loaders import TextLoader

loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()

```

```python
rds = Redis.from_documents(docs, embeddings, redis_url="redis://localhost:6379",  index_name='link')

```

```python
rds.index_name

```

```python
'link'

```

```python
query = "What did the president say about Ketanji Brown Jackson"
results = rds.similarity_search(query)
print(results[0].page_content)

```

```python
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. 

Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. 

One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. 

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.

```

```python
print(rds.add_texts(["Ankush went to Princeton"]))

```

```python
['doc:link:d7d02e3faf1b40bbbe29a683ff75b280']

```

```python
query = "Princeton"
results = rds.similarity_search(query)
print(results[0].page_content)

```

```python
Ankush went to Princeton

```

```python
# Load from existing index
rds = Redis.from_existing_index(embeddings, redis_url="redis://localhost:6379", index_name='link')

query = "What did the president say about Ketanji Brown Jackson"
results = rds.similarity_search(query)
print(results[0].page_content)

```

```python
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. 

Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. 

One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. 

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.

```

RedisVectorStoreRetriever[#](#redisvectorstoreretriever "Permalink to this headline")
-------------------------------------------------------------------------------------

这里我们讨论使用向量存储作为检索器的不同选项。

我们可以使用三种不同的搜索方法来进行检索。默认情况下，它将使用语义相似性。

```python
retriever = rds.as_retriever()

```

```python
docs = retriever.get_relevant_documents(query)

```

我们还可以使用similarity_limit作为搜索方法。只有当它们足够相似时，才会返回文档。

```python
retriever = rds.as_retriever(search_type="similarity_limit")

```

```python
# Here we can see it doesn't return any results because there are no relevant documents
retriever.get_relevant_documents("where did ankush go to college?")

```

